When calling word_tokenize I get the following error:
File "C:\Python34\lib\site-packages\nltk\tokenize\punkt.py", line 1322,
in _slices_from_text for match in
self._lang_vars.period_context_re().finditer(text):
TypeError: expected string or buffer
I have a large text file (1500.txt) from which I want to remove stop words.
My code is as follows:
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
with open('E:\\Book\\1500.txt', "r", encoding='ISO-8859-1') as File_1500:
stop_words = set(stopwords.words("english"))
words = word_tokenize(File_1500)
filtered_sentence = [w for w in words if not w in stop_words]
print(filtered_sentence)
The input for word_tokenize is a document stream sentence, i.e. a list of strings, e.g. ['this is sentence 1.', 'that's sentence 2!'].
The File_1500 is a File object not a list of strings, that's why it's not working.
To get a list of sentence strings, first you have to read the file as a string object fin.read(), then use sent_tokenize to split the sentence up (I'm assuming that your input file is not sentence tokenized, just a raw textfile).
Also, it's better / more idiomatic to tokenize a file this way with NLTK:
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize, sent_tokenize
stop_words = set(stopwords.words("english"))
with open('E:\\Book\\1500.txt', "r", encoding='ISO-8859-1') as fin:
for sent in sent_tokenize(fin.read()):
words = word_tokenize(sent)
filtered_sentence = [w for w in words if not w in stop_words]
print(filtered_sentence)
